Advanced data analytics does not live only in the realm of data scientists. The necessary tools now exist in SQL Server 2016 to perform advanced analytical analysis with R and Power BI. Attendees will see how to apply these techniques to analyze a database server, by showing how to perform in-depth analysis on practical things such as improving database monitoring, create predictive models for server performance load, and determining when disk space is required. Session Goals Learn how to code applications in R to provide data insight and data visualizations for use within SQL Server. Develop and understanding of some of the algorithms used in data science and how to apply them. Extend the functionality of SQL Server by integrating R code to provide insight into the performance of SQL Server. Understand different ways to visualize the results, including storing within SQL Server, creating SSRS reports and visualizing in Power BI.Session Agenda Introduction to Data Science concepts. Application of Data Science algorithms in R. SQL Server R Internals and Integration. Modifying SQL Server to optimally perform and monitor R Code. In depth walkthrough of SQL Server running R Introduction to Linear Regression. Application of linear regression to determine storage space usage over time within SQL Server. Predictive and Prescriptive Analysis techniques. Application of predictive and prescriptive analysis through analysis of DMVs. Common visualization techniques used in data science. Demonstration of how visualizations are used in evaluating data algorithms. Configuration and methods for displaying visualizations within SQL Server, R and Power BI.

Ginger Grant

Ginger Grant started working in business intelligence for a number of industries including transportation, insurance, and healthcare. Looking for answers in data led to advanced analytics and data science, where she currently creates systems to provide solutions to data questions. An active member of the data community, she is a MS MVP in Data Platform.